create_data
Doug McNeall
9/5/2023
source("~/jules_ppe_gmd/docs/JULES-ES-1p0-common-packages.R")## Loading required package: spam## Loading required package: dotCall64## Loading required package: grid## Spam version 2.7-0 (2021-06-25) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attaching package: 'spam'## The following objects are masked from 'package:base':
##
## backsolve, forwardsolve## Loading required package: viridis## Loading required package: viridisLite## See https://github.com/NCAR/Fields for
## an extensive vignette, other supplements and source code## Loading required package: lhs## Loading required package: parallel## Loading required package: viztoolssource("~/jules_ppe_gmd/docs/JULES-ES-1p0-common-functions.R")
## ----------------------------------------------------------------------
## Data locations and constants
## ----------------------------------------------------------------------
#ensloc <- '/project/carbon_ppe/JULES-ES-1p0_PPE/'
ensloc_wave00 <- '/data/users/hadaw/JULES_ES_PPE/u-au932/'
ensloc_wave01 <- '/data/users/hadaw/JULES_ES_PPE/u-ck006/'
# Some pallete options
yg = brewer.pal(9, "YlGn")
ryb = brewer.pal(11, "RdYlBu")
byr = rev(ryb)
rb = brewer.pal(11, "RdBu")
br = rev(rb)
blues = brewer.pal(9, 'Blues')
cbPal <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
wave00col <- 'skyblue2'
wave01col <- 'tomato2'
zissou5 <- wes_palette('Zissou1', 5, type = c('discrete', 'continuous'))
zblue <- makeTransparent(as.character(zissou5)[1], 150)
zred <- makeTransparent(as.character(zissou5)[5], 150)
ysec = 60*60*24*365
years <- 1850:2013global_mean_modern_value_wave00_file <- "~/jules_ppe_gmd/docs/data/global_mean_modern_value_wave00_2022-13-09.rdata"
load(global_mean_modern_value_wave00_file)
modern_value_stan_file <- "~/jules_ppe_gmd/docs/data/modern_value_stan_2022-09-13.rdata"
load(modern_value_stan_file)
Y <- datmat_wave00
Y_nlevel0_ix <- which(is.na(datmat_wave00[,'year']))# Load up the data
lhs_i = read.table('~/jules_ppe_gmd/docs/data/lhs_u-ao732.txt', header = TRUE)
lhs_ii = read.table('~/jules_ppe_gmd/docs/data/lhs_u-ao732a.txt', header = TRUE)
toplevel_ix = 1:499
# The raw input data is a latin hypercube
lhs = rbind(lhs_i, lhs_ii)[toplevel_ix, ]
lhs_level0 <- lhs[-Y_nlevel0_ix,]
X = normalize(lhs)
colnames(X) = colnames(lhs)
X_level0 <- X[-Y_nlevel0_ix,]
X_nlevel0 <- X[Y_nlevel0_ix,]
d = ncol(X)
# lower and higher bound on the normalised matrix for visualisation
rx = rbind(rep(0,32), rep(1,32))low_npp_ix <- which(Y[,'npp_nlim_lnd_sum'] < 1e5)
# code from https://stackoverflow.com/questions/28182872/how-to-use-different-sets-of-data-in-lower-and-upper-panel-of-pairs-function-in
#X <- matrix(runif(300), ncol=3)
#Y <- matrix(c(sort(runif(100, 0, 10)),
# sort(runif(100, 0, 10)),
# sort(runif(100, 0, 10))), ncol=3)
#pdf(file = 'figs/fig02.pdf', width = 12, height = 10)
#pdf(file = 'figs/run-failure-pairs.pdf', width = 12, height = 10)
x1 <- X[low_npp_ix, ]
x2 <- X_nlevel0
XY <- rbind(x1, x2)
pairs(XY,
lower.panel=function(x, y, ...) {
Xx <- x[seq_len(nrow(x1))] # corresponds to X subset
Xy <- y[seq_len(nrow(x1))] # corresponds to X subset
#usr <- par("usr"); on.exit(par(usr))
#par(usr = c(range(x1[, -ncol(x1)]), range(x1[, -1]))) # set up limits
points(Xx, Xy, col = zblue, pch = 19, cex = 0.8)
# if(par('mfg')[2] == 1) axis(2) # if left plot, add left axis
#if(par('mfg')[1] == ncol(x1)) axis(1) # if bottom plot add bottom axis
},
upper.panel=function(x, y, ...) {
Yx <- x[(nrow(x1) + 1):length(x)] # Y subset
Yy <- y[(nrow(x1) + 1):length(y)] # Y subset
#cntr <- outer(Yx, Yx, FUN='*') # arbitrary function for contour
# usr <- par("usr"); on.exit(par(usr))
#par(usr = c(range(x2[, -1]), range(x2[, -ncol(x2)]))) # set up limits
points(Yx, Yy, col = zred, pch = 19, cex = 0.8)
#contour(Yx, Yy, cntr, add=TRUE)
#if(par('mfg')[2] == ncol(x2)) axis(4) # if right plot, add right axis
#if(par('mfg')[1] == 1) axis(3) # if top plot, add top axis
},
#tick=FALSE, # suppress the default tick marks
#line=1,
gap = 0,
xlim = c(0,1), ylim = c(0,1),
labels = 1:d,
oma = c(2, 18, 2, 2)) # move the default tick labels off the plot
reset()
legend('left', legend = paste(1:d, colnames(lhs)), cex = 1.1, bty = 'n')
legend('topleft', pch = 19, col = c( zred, zblue), legend = c('failed', 'zero carbon cycle'), bty = 'n', inset = 0.02, cex = 1.1 )
#dev.off()Generate the data to save
Y_char <- rep('RAN', nrow(X))
Y_char[Y_nlevel0_ix] <- 'CRASHED'
Y_char[low_npp_ix] <- 'LOWCARBON'
Y_factor <- as.factor(Y_char)
wave0_summary_df <- cbind(X, Y, Y_char, Y_factor)save(lhs, X, Y, Y_char, Y_factor, wave0_summary_df, file = 'data/wave0_summary.RData')Data checks
Have we got the factors right?
cbind(Y[, 'npp_nlim_lnd_sum'], Y_char)## Y_char
## [1,] "2464953.47849605" "RAN"
## [2,] "1248000.44246179" "RAN"
## [3,] "1893850.67745132" "RAN"
## [4,] "1779634.61879458" "RAN"
## [5,] "1333052.53686655" "RAN"
## [6,] "1829087.04066031" "RAN"
## [7,] "125.100277029329" "LOWCARBON"
## [8,] "1354509.40828833" "RAN"
## [9,] "1436905.38963356" "RAN"
## [10,] "1105586.84200311" "RAN"
## [11,] "2281524.08799275" "RAN"
## [12,] "1569806.15413172" "RAN"
## [13,] "1452198.55278638" "RAN"
## [14,] "1199097.02939196" "RAN"
## [15,] "2617350.39816265" "RAN"
## [16,] "1249447.10148417" "RAN"
## [17,] "939242.694289745" "RAN"
## [18,] "5.37100446251452" "LOWCARBON"
## [19,] "1077579.05514713" "RAN"
## [20,] "1523180.02583773" "RAN"
## [21,] "1569417.63070336" "RAN"
## [22,] "1873231.77735771" "RAN"
## [23,] "1912583.54670419" "RAN"
## [24,] "1902959.37750349" "RAN"
## [25,] "1445375.9153619" "RAN"
## [26,] "951627.465373486" "RAN"
## [27,] "1662356.03945454" "RAN"
## [28,] "1936958.91626488" "RAN"
## [29,] "1433603.10792285" "RAN"
## [30,] "976651.168260095" "RAN"
## [31,] "2082565.02331613" "RAN"
## [32,] "1531283.77037663" "RAN"
## [33,] "1645926.53544732" "RAN"
## [34,] "1320236.28995648" "RAN"
## [35,] "2891640.62239662" "RAN"
## [36,] "1635834.14721978" "RAN"
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## [40,] "651768.709997753" "RAN"
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## [44,] "1061709.94504286" "RAN"
## [45,] "1775343.21596816" "RAN"
## [46,] "2311775.26816144" "RAN"
## [47,] "1441874.46802137" "RAN"
## [48,] "2195581.99198865" "RAN"
## [49,] "1325496.4911125" "RAN"
## [50,] "2004112.56580796" "RAN"
## [51,] "2368885.55711663" "RAN"
## [52,] "1361.54882660815" "LOWCARBON"
## [53,] "908731.084399189" "RAN"
## [54,] "1442169.69546151" "RAN"
## [55,] "139.629393179477" "LOWCARBON"
## [56,] "1333927.55011543" "RAN"
## [57,] "1666849.46116248" "RAN"
## [58,] "965867.767068223" "RAN"
## [59,] "1243866.85654177" "RAN"
## [60,] "1943266.73141755" "RAN"
## [61,] "1902138.05825955" "RAN"
## [62,] "1767409.94121306" "RAN"
## [63,] "1317749.66685722" "RAN"
## [64,] "1199841.24949906" "RAN"
## [65,] "1837154.16360185" "RAN"
## [66,] "1163145.1259366" "RAN"
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## [84,] "2100616.21162727" "RAN"
## [85,] "1776678.07569972" "RAN"
## [86,] "836.397054014272" "LOWCARBON"
## [87,] NA "CRASHED"
## [88,] "438550.74712463" "RAN"
## [89,] "1279.62367587908" "LOWCARBON"
## [90,] "1524159.38243703" "RAN"
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## [94,] "27456.120078662" "LOWCARBON"
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## [97,] "80867.6892480567" "LOWCARBON"
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## [99,] "1233881.05138578" "RAN"
## [100,] "1443212.18749931" "RAN"
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## [102,] "22.6523656647353" "LOWCARBON"
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## [107,] "2387524.55902676" "RAN"
## [108,] "1808140.75254821" "RAN"
## [109,] "3034612.74359176" "RAN"
## [110,] "1305467.18461631" "RAN"
## [111,] "2215.47450906594" "LOWCARBON"
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## [120,] "1304973.40986444" "RAN"
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## [124,] "2997.38035283134" "LOWCARBON"
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## [126,] "2988253.78284404" "RAN"
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## [132,] "2163311.88964093" "RAN"
## [133,] "2178050.99534827" "RAN"
## [134,] "566076.622930143" "RAN"
## [135,] "1392961.37882784" "RAN"
## [136,] "18392.8206574553" "LOWCARBON"
## [137,] "1947440.97630319" "RAN"
## [138,] "2636818.64751266" "RAN"
## [139,] "28499.059770153" "LOWCARBON"
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## [141,] "0" "LOWCARBON"
## [142,] "835144.438570216" "RAN"
## [143,] "2128420.55576015" "RAN"
## [144,] "1521308.9730314" "RAN"
## [145,] NA "CRASHED"
## [146,] "2748024.4267935" "RAN"
## [147,] "973221.738622719" "RAN"
## [148,] "860049.23757151" "RAN"
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## [150,] "1201010.03107696" "RAN"
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## [152,] NA "CRASHED"
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## [159,] "16.1599645777015" "LOWCARBON"
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## [168,] "1990818.87407025" "RAN"
## [169,] "2257432.14856896" "RAN"
## [170,] "1550902.99283446" "RAN"
## [171,] NA "CRASHED"
## [172,] "8616.48535791419" "LOWCARBON"
## [173,] "2063335.56382181" "RAN"
## [174,] "2217.33780499236" "LOWCARBON"
## [175,] "1455273.15033427" "RAN"
## [176,] "1602432.8716883" "RAN"
## [177,] "1442763.37340397" "RAN"
## [178,] "935002.418487836" "RAN"
## [179,] NA "CRASHED"
## [180,] "2398227.96374005" "RAN"
## [181,] "2257472.96108099" "RAN"
## [182,] "1463089.42164426" "RAN"
## [183,] "968805.302928887" "RAN"
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## [185,] "202447.955948166" "RAN"
## [186,] "1436879.80366549" "RAN"
## [187,] "1050598.86322165" "RAN"
## [188,] "858303.300545437" "RAN"
## [189,] "837.8470560364" "LOWCARBON"
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## [192,] "1442330.64357137" "RAN"
## [193,] "300301.613025114" "RAN"
## [194,] "1867448.54134229" "RAN"
## [195,] "2318123.19343285" "RAN"
## [196,] "1192558.07059905" "RAN"
## [197,] "296580.206341884" "RAN"
## [198,] "198948.53460339" "RAN"
## [199,] "1475476.98479445" "RAN"
## [200,] "1151974.92734534" "RAN"
## [201,] "1045780.41572662" "RAN"
## [202,] "597.349982204987" "LOWCARBON"
## [203,] "2088874.69446935" "RAN"
## [204,] "1001228.25695345" "RAN"
## [205,] "1163274.13917084" "RAN"
## [206,] NA "CRASHED"
## [207,] "16.9065379434552" "LOWCARBON"
## [208,] "1719257.89125913" "RAN"
## [209,] "1223443.31154094" "RAN"
## [210,] "436285.944826903" "RAN"
## [211,] "2517584.52687686" "RAN"
## [212,] "1147738.92352667" "RAN"
## [213,] "16630.6275934419" "LOWCARBON"
## [214,] "1136523.58134658" "RAN"
## [215,] "2945929.85082532" "RAN"
## [216,] "1027358.29145306" "RAN"
## [217,] "1090954.7293875" "RAN"
## [218,] NA "CRASHED"
## [219,] "474.410621824389" "LOWCARBON"
## [220,] "1182142.21931005" "RAN"
## [221,] "1244732.72703289" "RAN"
## [222,] "817223.408383973" "RAN"
## [223,] "789494.766979139" "RAN"
## [224,] "1286529.489797" "RAN"
## [225,] "771006.894030684" "RAN"
## [226,] "778690.42112912" "RAN"
## [227,] "177.882608699528" "LOWCARBON"
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## [229,] "1225.95556037021" "LOWCARBON"
## [230,] NA "CRASHED"
## [231,] "1756439.8256892" "RAN"
## [232,] "14878.4358912422" "LOWCARBON"
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## [236,] "5418.55514931159" "LOWCARBON"
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## [238,] "709437.349215471" "RAN"
## [239,] NA "CRASHED"
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## [241,] "88222.8675543907" "LOWCARBON"
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## [248,] "1128079.61589957" "RAN"
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## [250,] "2726652.51399176" "RAN"
## [251,] "21571.315698287" "LOWCARBON"
## [252,] NA "CRASHED"
## [253,] "1961574.09889377" "RAN"
## [254,] "47946.6186543254" "LOWCARBON"
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## [256,] "2251606.48396996" "RAN"
## [257,] "3273070.39797162" "RAN"
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## [259,] "586088.226578541" "RAN"
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## [261,] NA "CRASHED"
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## [263,] NA "CRASHED"
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## [290,] "26186.6324336018" "LOWCARBON"
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## [293,] "3378889.69905921" "RAN"
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## [296,] "853512.111943073" "RAN"
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